English(EN) IQS-BO: In-Context Query Selection for Bayesian Optimisation

新的IQS-BO方法通过学习到的查询选择来简化贝叶斯优化

作者 PulseAugur 编辑部 · [1 个来源] · 2026-10-02 04:00

研究人员推出了一种新颖的贝叶斯优化方法IQS-BO，该方法显著降低了计算成本。与需要重复拟合代理模型的传统方法不同，IQS-BO利用先验数据拟合网络（PFNs）通过在合成数据上进行监督学习来学习查询决策。这使得单次前向传播即可预测候选者最大化目标的概率，在各种基准测试中优于现有方法。 AI

影响这种用于贝叶斯优化的新方法可以加速在各种AI应用中优化昂贵的黑盒函数的过程。

排序理由该集群包含一篇详细介绍贝叶斯优化新方法的学术论文。[lever_c_demoted from research: ic=1 ai=1.0]

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arXiv stat.ML TIER_1 English(EN) · Luca Geminiani, Nadja Klein · 2026-10-02 04:00

IQS-BO：贝叶斯优化的上下文内查询选择

arXiv:2610.01269v1 Announce Type: cross Abstract: Bayesian Optimisation (BO) is a powerful framework for the optimisation of expensive black-box functions, but typically requires refitting a surrogate and maximising an acquisition function at every evaluation step. In-context app…